Role Description Intrado is looking to hire an Associate Data Analyst to join our team. The Associate Data Analyst plays a key role in managing and validating data to support business operations. This position involves handling incoming cases, ensuring accurate documentation, and coordinating with internal teams to drive resolution. With a focus on efficiency and accuracy, the role requires strong analytical skills, attention to detail, and the ability to escalate complex issues to senior staff when necessary. - Process cases as they enter the department - Uses pattern‑matching techniques (such as regular expressions) to validate raw data and detect anomalies. - Identify cases that need to be escalated to other staff such as Data Engineers and Senior Data Engineers - Provide department staff with documentation for cases, when requested - Coordinate with internal teams to provide complete solutions for cases - Escalate undocumented cases for further research and processing - Complete various data validation tasks Qualifications - High school diploma or GED required - Some College Experience Preferred Requirements - 1-year data collection or data entry experience preferred - Basic knowledge of MS Word, PowerPoint, Excel, and Outlook required - Basic knowledge of MS SQL preferred - Ability to see and analyze patterns in large sets of data - Must successfully meet the minimum score on a data entry test to qualify Benefits - Medical, dental, vision, life and disability coverage - Paid time off - 401(k) retirement plan - Paid parental leave - Access to a robust library of personal and professional training resources - Employee discounts - Critical illness and hospital indemnity coverage - Access to legal support - Pet insurance - Identity theft protection - EAP (Employee Assistance Program) that includes free mental health resources/support The starting salary is anticipated between $21 and $24 an hour and will be commensurate with experience.

• Lead, coach, and develop a team of approximately 8 data analysts supporting Pricing & Supply initiatives. • Partner closely with leaders across Pricing, Supply, Product and Data to define analytical priorities and drive measurable business impact. • Build a strong analytics operating model, including prioritization, stakeholder management, quality standards, reusable reporting, and scalable insight generation. • Translate ambiguous business questions into clear analytical plans, decision frameworks, dashboards, experiments, and recommendations. • Help the business better understand pricing performance, supplier coverage, marketplace dynamics, conversion, margin, customer behavior, and operational levers. • Raise the bar for analytical rigor across the team, including metric definition, data storytelling, root-cause analysis, experimentation, and forecasting. • Develop analysts into stronger business partners who can influence decisions, communicate clearly, and operate with ownership. • Identify opportunities to improve the data foundation, partnering with data engineering and analytics engineering teams to improve tooling, data models, and self-service capabilities.

• Support the pre and post-sales motion across account management, customer success, and RevOps to drive business value. • Track and report on critical post-sales metrics, including customer health, renewal risk, and expansion revenue. • Partner with stakeholders across account management, customer success, and RevOps to lead metrics definition, reporting, and analyses. • Lead key analytics projects and derive actionable insights to drive better retention and expansion performance. • Build and maintain repeatable reporting workflows and data pipelines to track relevant product and business metrics. • Manage the company’s business intelligence reporting dashboards to provide visibility into customer health and revenue trends.

• Support Ecosystem Analytics: Own the reporting and optimization of Case Volume and Resolution Rates. You will identify bottlenecks in our support funnel and recommend strategies to decrease "Time to Resolve." • Platform Vigilance: Monitor real-time data to detect site issues or technical anomalies that impact user experience, acting as the bridge between Client Ops and Engineering for rapid incident response. • Fraud & Risk Mitigation: Develop and maintain dashboards to track suspicious activity and fraud patterns, ensuring the platform remains secure for all legitimate users. • Operational Intelligence: Use Tableau to build comprehensive visualizations that allow leadership to see "at a glance" how effectively we are serving our clients and where the biggest operational risks lie. • Root Cause Analysis: Investigate why certain issues recur. You won’t just report that volume is up; you’ll identify if it’s a product bug, a training gap, or a seasonal trend. • Data Pipeline Advocacy: Partner with Data Engineering to ensure that data from our support tools and site logs are correctly ingested into Snowflake for deep-dive analysis. • Strategic Recommendations: Translate complex operational data into clear, high-impact insights for senior leadership, focusing on how to scale our service model without increasing headcount linearly.